Face detection has emerged as a cornerstone task in modern computer vision, forming the backbone of numerous real-world applications, including biometric authentication, security and surveillance systems, smartphone unlocking features, and social media tagging. Over the past two decades, significant advancements have been made in this field, evolving from early handcrafted feature-based algorithms to sophisticated deep learning architectures capable of handling complex scenarios. Despite these advancements, designing a robust and efficient face detection system that performs reliably under diverse conditions remains a challenging problem. This project aims to design and implement a comprehensive face detection system by integrating both traditional and modern methodologies. The proposed approach involves careful dataset selection, rigorous preprocessing, and the application of classical techniques such as Haar Cascade alongside modern convolutional neural network (CNN)-based frameworks, including Multi-task Cascaded Convolutional Networks (MTCNN) and YOLO-based detectors. Through this methodology, the project evaluates the performance, accuracy, and real-time efficiency of different detection strategies. The results of the study demonstrate that the system achieves high efficiency in real-time detection while effectively identifying faces under varying poses, scales, and illumination conditions. However, certain challenges remain, including handling occlusions, extreme pose variations, and low-light scenarios, which continue to affect detection accuracy. The project concludes by suggesting future directions for improvement, such as incorporating bias mitigation strategies, exploring multimodal biometric systems, and implementing liveness detection to enhance security.